Man pages for MarioniLab/scran
Methods for Single-Cell RNA-Seq Data Analysis

bootstrapClusterAssess cluster stability by bootstrapping
buildSNNGraphBuild a nearest-neighbor graph
cleanSizeFactorsClean size factors
clusterModularityCompute the cluster-wise modularity
combineMarkersCombine pairwise DE results into a marker list
combinePValuesCombine p-values
combineVarCombine variance decompositions
computeSpikeFactorsNormalization with spike-in counts
computeSumFactorsNormalization by deconvolution
convertToConvert to other classes
correlateGenesPer-gene correlation statistics
correlateNullBuild null correlations
correlatePairsTest for significant correlations
cycloneCell cycle phase classification
denoisePCADenoise expression with PCA
DMCompute the distance-to-median statistic
doubletCellsDetect doublet cells
doubletClusterDetect doublet clusters
findMarkersFind marker genes
fitTrendCV2Fit a trend to the CV2
fitTrendPoissonGenerate a trend for Poisson noise
fitTrendVarFit a trend to the variances of log-counts
gene_selectionGene selection
getClusteredPCsUse clusters to choose the number of PCs
getTopHVGsIdentify HVGs
getTopMarkersGet top markers
modelGeneCV2Model the per-gene CV2
modelGeneCV2WithSpikesModel the per-gene CV2 with spike-ins
modelGeneVarModel the per-gene variance
modelGeneVarByPoissonModel the per-gene variance with spike-ins
modelGeneVarWithSpikesModel the per-gene variance with spike-ins
pairwiseBinomPerform pairwise binomial tests
pairwiseTTestsPerform pairwise t-tests
pairwiseWilcoxPerform pairwise Wilcoxon rank sum tests
quickClusterQuick clustering of cells
quickSubClusterQuick and dirty subclustering
sandbagCell cycle phase training
scaledColRanksCompute scaled column ranks
MarioniLab/scran documentation built on Nov. 6, 2019, 2:46 p.m.